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| # DetectNet network | |
| # Data/Input layers | |
| name: "DetectNet" | |
| layer { | |
| name: "train_data" | |
| type: "Data" | |
| top: "data" | |
| data_param { | |
| backend: LMDB | |
| source: "examples/kitti/kitti_train_images.lmdb" | |
| batch_size: 10 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layer { | |
| name: "train_label" | |
| type: "Data" | |
| top: "label" | |
| data_param { | |
| backend: LMDB | |
| source: "examples/kitti/kitti_train_labels.lmdb" | |
| batch_size: 10 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layer { | |
| name: "val_data" | |
| type: "Data" | |
| top: "data" | |
| data_param { | |
| backend: LMDB | |
| source: "examples/kitti/kitti_test_images.lmdb" | |
| batch_size: 6 | |
| } | |
| include: { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "val_label" | |
| type: "Data" | |
| top: "label" | |
| data_param { | |
| backend: LMDB | |
| source: "examples/kitti/kitti_test_labels.lmdb" | |
| batch_size: 6 | |
| } | |
| include: { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "deploy_data" | |
| type: "Input" | |
| top: "data" | |
| input_param { | |
| shape { | |
| dim: 1 | |
| dim: 3 | |
| dim: 384 | |
| dim: 1248 | |
| } | |
| } | |
| include: { phase: TEST not_stage: "val" } | |
| } | |
| # Data transformation layers | |
| layer { | |
| name: "train_transform" | |
| type: "DetectNetTransformation" | |
| bottom: "data" | |
| bottom: "label" | |
| top: "transformed_data" | |
| top: "transformed_label" | |
| detectnet_groundtruth_param: { | |
| stride: 16 | |
| scale_cvg: 0.4 | |
| gridbox_type: GRIDBOX_MIN | |
| coverage_type: RECTANGULAR | |
| min_cvg_len: 20 | |
| obj_norm: true | |
| image_size_x: 1248 | |
| image_size_y: 384 | |
| crop_bboxes: true | |
| object_class: { src: 1 dst: 0} # obj class 1 -> cvg index 0 | |
| } | |
| detectnet_augmentation_param: { | |
| crop_prob: 1 | |
| shift_x: 32 | |
| shift_y: 32 | |
| flip_prob: 0.5 | |
| rotation_prob: 0 | |
| max_rotate_degree: 5 | |
| scale_prob: 0.4 | |
| scale_min: 0.8 | |
| scale_max: 1.2 | |
| hue_rotation_prob: 0.8 | |
| hue_rotation: 30 | |
| desaturation_prob: 0.8 | |
| desaturation_max: 0.8 | |
| } | |
| transform_param: { | |
| mean_value: 127 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layer { | |
| name: "val_transform" | |
| type: "DetectNetTransformation" | |
| bottom: "data" | |
| bottom: "label" | |
| top: "transformed_data" | |
| top: "transformed_label" | |
| detectnet_groundtruth_param: { | |
| stride: 16 | |
| scale_cvg: 0.4 | |
| gridbox_type: GRIDBOX_MIN | |
| coverage_type: RECTANGULAR | |
| min_cvg_len: 20 | |
| obj_norm: true | |
| image_size_x: 1248 | |
| image_size_y: 384 | |
| crop_bboxes: false | |
| object_class: { src: 1 dst: 0} # obj class 1 -> cvg index 0 | |
| } | |
| transform_param: { | |
| mean_value: 127 | |
| } | |
| include: { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "deploy_transform" | |
| type: "Power" | |
| bottom: "data" | |
| top: "transformed_data" | |
| power_param { | |
| shift: -127 | |
| } | |
| include: { phase: TEST not_stage: "val" } | |
| } | |
| # Label conversion layers | |
| layer { | |
| name: "slice-label" | |
| type: "Slice" | |
| bottom: "transformed_label" | |
| top: "foreground-label" | |
| top: "bbox-label" | |
| top: "size-label" | |
| top: "obj-label" | |
| top: "coverage-label" | |
| slice_param { | |
| slice_dim: 1 | |
| slice_point: 1 | |
| slice_point: 5 | |
| slice_point: 7 | |
| slice_point: 8 | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "coverage-block" | |
| type: "Concat" | |
| bottom: "foreground-label" | |
| bottom: "foreground-label" | |
| bottom: "foreground-label" | |
| bottom: "foreground-label" | |
| top: "coverage-block" | |
| concat_param { | |
| concat_dim: 1 | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "size-block" | |
| type: "Concat" | |
| bottom: "size-label" | |
| bottom: "size-label" | |
| top: "size-block" | |
| concat_param { | |
| concat_dim: 1 | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "obj-block" | |
| type: "Concat" | |
| bottom: "obj-label" | |
| bottom: "obj-label" | |
| bottom: "obj-label" | |
| bottom: "obj-label" | |
| top: "obj-block" | |
| concat_param { | |
| concat_dim: 1 | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "bb-label-norm" | |
| type: "Eltwise" | |
| bottom: "bbox-label" | |
| bottom: "size-block" | |
| top: "bbox-label-norm" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "bb-obj-norm" | |
| type: "Eltwise" | |
| bottom: "bbox-label-norm" | |
| bottom: "obj-block" | |
| top: "bbox-obj-label-norm" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| ###################################################################### | |
| # Start of convolutional network | |
| ###################################################################### | |
| layer { | |
| name: "conv1/7x7_s2" | |
| type: "Convolution" | |
| bottom: "transformed_data" | |
| top: "conv1/7x7_s2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 3 | |
| kernel_size: 7 | |
| stride: 2 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/relu_7x7" | |
| type: "ReLU" | |
| bottom: "conv1/7x7_s2" | |
| top: "conv1/7x7_s2" | |
| } | |
| layer { | |
| name: "pool1/3x3_s2" | |
| type: "Pooling" | |
| bottom: "conv1/7x7_s2" | |
| top: "pool1/3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "pool1/norm1" | |
| type: "LRN" | |
| bottom: "pool1/3x3_s2" | |
| top: "pool1/norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "conv2/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "pool1/norm1" | |
| top: "conv2/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "conv2/3x3_reduce" | |
| top: "conv2/3x3_reduce" | |
| } | |
| layer { | |
| name: "conv2/3x3" | |
| type: "Convolution" | |
| bottom: "conv2/3x3_reduce" | |
| top: "conv2/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/relu_3x3" | |
| type: "ReLU" | |
| bottom: "conv2/3x3" | |
| top: "conv2/3x3" | |
| } | |
| layer { | |
| name: "conv2/norm2" | |
| type: "LRN" | |
| bottom: "conv2/3x3" | |
| top: "conv2/norm2" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool2/3x3_s2" | |
| type: "Pooling" | |
| bottom: "conv2/norm2" | |
| top: "pool2/3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/1x1" | |
| type: "Convolution" | |
| bottom: "pool2/3x3_s2" | |
| top: "inception_3a/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_3a/1x1" | |
| top: "inception_3a/1x1" | |
| } | |
| layer { | |
| name: "inception_3a/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "pool2/3x3_s2" | |
| top: "inception_3a/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3a/3x3_reduce" | |
| top: "inception_3a/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_3a/3x3" | |
| type: "Convolution" | |
| bottom: "inception_3a/3x3_reduce" | |
| top: "inception_3a/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_3a/3x3" | |
| top: "inception_3a/3x3" | |
| } | |
| layer { | |
| name: "inception_3a/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "pool2/3x3_s2" | |
| top: "inception_3a/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3a/5x5_reduce" | |
| top: "inception_3a/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_3a/5x5" | |
| type: "Convolution" | |
| bottom: "inception_3a/5x5_reduce" | |
| top: "inception_3a/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_3a/5x5" | |
| top: "inception_3a/5x5" | |
| } | |
| layer { | |
| name: "inception_3a/pool" | |
| type: "Pooling" | |
| bottom: "pool2/3x3_s2" | |
| top: "inception_3a/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_3a/pool" | |
| top: "inception_3a/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_3a/pool_proj" | |
| top: "inception_3a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_3a/output" | |
| type: "Concat" | |
| bottom: "inception_3a/1x1" | |
| bottom: "inception_3a/3x3" | |
| bottom: "inception_3a/5x5" | |
| bottom: "inception_3a/pool_proj" | |
| top: "inception_3a/output" | |
| } | |
| layer { | |
| name: "inception_3b/1x1" | |
| type: "Convolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_3b/1x1" | |
| top: "inception_3b/1x1" | |
| } | |
| layer { | |
| name: "inception_3b/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3b/3x3_reduce" | |
| top: "inception_3b/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_3b/3x3" | |
| type: "Convolution" | |
| bottom: "inception_3b/3x3_reduce" | |
| top: "inception_3b/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_3b/3x3" | |
| top: "inception_3b/3x3" | |
| } | |
| layer { | |
| name: "inception_3b/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3b/5x5_reduce" | |
| top: "inception_3b/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_3b/5x5" | |
| type: "Convolution" | |
| bottom: "inception_3b/5x5_reduce" | |
| top: "inception_3b/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_3b/5x5" | |
| top: "inception_3b/5x5" | |
| } | |
| layer { | |
| name: "inception_3b/pool" | |
| type: "Pooling" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_3b/pool" | |
| top: "inception_3b/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_3b/pool_proj" | |
| top: "inception_3b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_3b/output" | |
| type: "Concat" | |
| bottom: "inception_3b/1x1" | |
| bottom: "inception_3b/3x3" | |
| bottom: "inception_3b/5x5" | |
| bottom: "inception_3b/pool_proj" | |
| top: "inception_3b/output" | |
| } | |
| layer { | |
| name: "pool3/3x3_s2" | |
| type: "Pooling" | |
| bottom: "inception_3b/output" | |
| top: "pool3/3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/1x1" | |
| type: "Convolution" | |
| bottom: "pool3/3x3_s2" | |
| top: "inception_4a/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4a/1x1" | |
| top: "inception_4a/1x1" | |
| } | |
| layer { | |
| name: "inception_4a/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "pool3/3x3_s2" | |
| top: "inception_4a/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4a/3x3_reduce" | |
| top: "inception_4a/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4a/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4a/3x3_reduce" | |
| top: "inception_4a/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 208 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4a/3x3" | |
| top: "inception_4a/3x3" | |
| } | |
| layer { | |
| name: "inception_4a/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "pool3/3x3_s2" | |
| top: "inception_4a/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4a/5x5_reduce" | |
| top: "inception_4a/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4a/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4a/5x5_reduce" | |
| top: "inception_4a/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4a/5x5" | |
| top: "inception_4a/5x5" | |
| } | |
| layer { | |
| name: "inception_4a/pool" | |
| type: "Pooling" | |
| bottom: "pool3/3x3_s2" | |
| top: "inception_4a/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4a/pool" | |
| top: "inception_4a/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4a/pool_proj" | |
| top: "inception_4a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4a/output" | |
| type: "Concat" | |
| bottom: "inception_4a/1x1" | |
| bottom: "inception_4a/3x3" | |
| bottom: "inception_4a/5x5" | |
| bottom: "inception_4a/pool_proj" | |
| top: "inception_4a/output" | |
| } | |
| layer { | |
| name: "inception_4b/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4b/1x1" | |
| top: "inception_4b/1x1" | |
| } | |
| layer { | |
| name: "inception_4b/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 112 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4b/3x3_reduce" | |
| top: "inception_4b/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4b/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4b/3x3_reduce" | |
| top: "inception_4b/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 224 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4b/3x3" | |
| top: "inception_4b/3x3" | |
| } | |
| layer { | |
| name: "inception_4b/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4b/5x5_reduce" | |
| top: "inception_4b/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4b/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4b/5x5_reduce" | |
| top: "inception_4b/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4b/5x5" | |
| top: "inception_4b/5x5" | |
| } | |
| layer { | |
| name: "inception_4b/pool" | |
| type: "Pooling" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4b/pool" | |
| top: "inception_4b/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4b/pool_proj" | |
| top: "inception_4b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4b/output" | |
| type: "Concat" | |
| bottom: "inception_4b/1x1" | |
| bottom: "inception_4b/3x3" | |
| bottom: "inception_4b/5x5" | |
| bottom: "inception_4b/pool_proj" | |
| top: "inception_4b/output" | |
| } | |
| layer { | |
| name: "inception_4c/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4c/1x1" | |
| top: "inception_4c/1x1" | |
| } | |
| layer { | |
| name: "inception_4c/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4c/3x3_reduce" | |
| top: "inception_4c/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4c/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4c/3x3_reduce" | |
| top: "inception_4c/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4c/3x3" | |
| top: "inception_4c/3x3" | |
| } | |
| layer { | |
| name: "inception_4c/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4c/5x5_reduce" | |
| top: "inception_4c/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4c/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4c/5x5_reduce" | |
| top: "inception_4c/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4c/5x5" | |
| top: "inception_4c/5x5" | |
| } | |
| layer { | |
| name: "inception_4c/pool" | |
| type: "Pooling" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4c/pool" | |
| top: "inception_4c/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4c/pool_proj" | |
| top: "inception_4c/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4c/output" | |
| type: "Concat" | |
| bottom: "inception_4c/1x1" | |
| bottom: "inception_4c/3x3" | |
| bottom: "inception_4c/5x5" | |
| bottom: "inception_4c/pool_proj" | |
| top: "inception_4c/output" | |
| } | |
| layer { | |
| name: "inception_4d/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 112 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4d/1x1" | |
| top: "inception_4d/1x1" | |
| } | |
| layer { | |
| name: "inception_4d/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 144 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4d/3x3_reduce" | |
| top: "inception_4d/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4d/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4d/3x3_reduce" | |
| top: "inception_4d/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 288 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4d/3x3" | |
| top: "inception_4d/3x3" | |
| } | |
| layer { | |
| name: "inception_4d/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4d/5x5_reduce" | |
| top: "inception_4d/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4d/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4d/5x5_reduce" | |
| top: "inception_4d/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4d/5x5" | |
| top: "inception_4d/5x5" | |
| } | |
| layer { | |
| name: "inception_4d/pool" | |
| type: "Pooling" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4d/pool" | |
| top: "inception_4d/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4d/pool_proj" | |
| top: "inception_4d/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4d/output" | |
| type: "Concat" | |
| bottom: "inception_4d/1x1" | |
| bottom: "inception_4d/3x3" | |
| bottom: "inception_4d/5x5" | |
| bottom: "inception_4d/pool_proj" | |
| top: "inception_4d/output" | |
| } | |
| layer { | |
| name: "inception_4e/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4e/1x1" | |
| top: "inception_4e/1x1" | |
| } | |
| layer { | |
| name: "inception_4e/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4e/3x3_reduce" | |
| top: "inception_4e/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4e/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4e/3x3_reduce" | |
| top: "inception_4e/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 320 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4e/3x3" | |
| top: "inception_4e/3x3" | |
| } | |
| layer { | |
| name: "inception_4e/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4e/5x5_reduce" | |
| top: "inception_4e/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4e/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4e/5x5_reduce" | |
| top: "inception_4e/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4e/5x5" | |
| top: "inception_4e/5x5" | |
| } | |
| layer { | |
| name: "inception_4e/pool" | |
| type: "Pooling" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4e/pool" | |
| top: "inception_4e/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4e/pool_proj" | |
| top: "inception_4e/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4e/output" | |
| type: "Concat" | |
| bottom: "inception_4e/1x1" | |
| bottom: "inception_4e/3x3" | |
| bottom: "inception_4e/5x5" | |
| bottom: "inception_4e/pool_proj" | |
| top: "inception_4e/output" | |
| } | |
| layer { | |
| name: "inception_5a/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4e/output" | |
| top: "inception_5a/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_5a/1x1" | |
| top: "inception_5a/1x1" | |
| } | |
| layer { | |
| name: "inception_5a/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4e/output" | |
| top: "inception_5a/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5a/3x3_reduce" | |
| top: "inception_5a/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_5a/3x3" | |
| type: "Convolution" | |
| bottom: "inception_5a/3x3_reduce" | |
| top: "inception_5a/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 320 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_5a/3x3" | |
| top: "inception_5a/3x3" | |
| } | |
| layer { | |
| name: "inception_5a/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4e/output" | |
| top: "inception_5a/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5a/5x5_reduce" | |
| top: "inception_5a/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_5a/5x5" | |
| type: "Convolution" | |
| bottom: "inception_5a/5x5_reduce" | |
| top: "inception_5a/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_5a/5x5" | |
| top: "inception_5a/5x5" | |
| } | |
| layer { | |
| name: "inception_5a/pool" | |
| type: "Pooling" | |
| bottom: "inception_4e/output" | |
| top: "inception_5a/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_5a/pool" | |
| top: "inception_5a/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_5a/pool_proj" | |
| top: "inception_5a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_5a/output" | |
| type: "Concat" | |
| bottom: "inception_5a/1x1" | |
| bottom: "inception_5a/3x3" | |
| bottom: "inception_5a/5x5" | |
| bottom: "inception_5a/pool_proj" | |
| top: "inception_5a/output" | |
| } | |
| layer { | |
| name: "inception_5b/1x1" | |
| type: "Convolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_5b/1x1" | |
| top: "inception_5b/1x1" | |
| } | |
| layer { | |
| name: "inception_5b/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/3x3_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5b/3x3_reduce" | |
| top: "inception_5b/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_5b/3x3" | |
| type: "Convolution" | |
| bottom: "inception_5b/3x3_reduce" | |
| top: "inception_5b/3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_5b/3x3" | |
| top: "inception_5b/3x3" | |
| } | |
| layer { | |
| name: "inception_5b/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/5x5_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5b/5x5_reduce" | |
| top: "inception_5b/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_5b/5x5" | |
| type: "Convolution" | |
| bottom: "inception_5b/5x5_reduce" | |
| top: "inception_5b/5x5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_5b/5x5" | |
| top: "inception_5b/5x5" | |
| } | |
| layer { | |
| name: "inception_5b/pool" | |
| type: "Pooling" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_5b/pool" | |
| top: "inception_5b/pool_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_5b/pool_proj" | |
| top: "inception_5b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_5b/output" | |
| type: "Concat" | |
| bottom: "inception_5b/1x1" | |
| bottom: "inception_5b/3x3" | |
| bottom: "inception_5b/5x5" | |
| bottom: "inception_5b/pool_proj" | |
| top: "inception_5b/output" | |
| } | |
| layer { | |
| name: "pool5/drop_s1" | |
| type: "Dropout" | |
| bottom: "inception_5b/output" | |
| top: "pool5/drop_s1" | |
| dropout_param { | |
| dropout_ratio: 0.4 | |
| } | |
| } | |
| layer { | |
| name: "cvg/classifier" | |
| type: "Convolution" | |
| bottom: "pool5/drop_s1" | |
| top: "cvg/classifier" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "coverage/sig" | |
| type: "Sigmoid" | |
| bottom: "cvg/classifier" | |
| top: "coverage" | |
| } | |
| layer { | |
| name: "bbox/regressor" | |
| type: "Convolution" | |
| bottom: "pool5/drop_s1" | |
| top: "bboxes" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 4 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| ###################################################################### | |
| # End of convolutional network | |
| ###################################################################### | |
| # Convert bboxes | |
| layer { | |
| name: "bbox_mask" | |
| type: "Eltwise" | |
| bottom: "bboxes" | |
| bottom: "coverage-block" | |
| top: "bboxes-masked" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "bbox-norm" | |
| type: "Eltwise" | |
| bottom: "bboxes-masked" | |
| bottom: "size-block" | |
| top: "bboxes-masked-norm" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "bbox-obj-norm" | |
| type: "Eltwise" | |
| bottom: "bboxes-masked-norm" | |
| bottom: "obj-block" | |
| top: "bboxes-obj-masked-norm" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| # Loss layers | |
| layer { | |
| name: "bbox_loss" | |
| type: "L1Loss" | |
| bottom: "bboxes-obj-masked-norm" | |
| bottom: "bbox-obj-label-norm" | |
| top: "loss_bbox" | |
| loss_weight: 2 | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| name: "coverage_loss" | |
| type: "EuclideanLoss" | |
| bottom: "coverage" | |
| bottom: "coverage-label" | |
| top: "loss_coverage" | |
| include { phase: TRAIN } | |
| include { phase: TEST stage: "val" } | |
| } | |
| # Cluster bboxes | |
| layer { | |
| type: 'Python' | |
| name: 'cluster' | |
| bottom: 'coverage' | |
| bottom: 'bboxes' | |
| top: 'bbox-list' | |
| python_param { | |
| module: 'caffe.layers.detectnet.clustering' | |
| layer: 'ClusterDetections' | |
| param_str : '1248, 352, 16, 0.6, 3, 0.02, 22, 1' | |
| } | |
| include: { phase: TEST } | |
| } | |
| # Calculate mean average precision | |
| layer { | |
| type: 'Python' | |
| name: 'cluster_gt' | |
| bottom: 'coverage-label' | |
| bottom: 'bbox-label' | |
| top: 'bbox-list-label' | |
| python_param { | |
| module: 'caffe.layers.detectnet.clustering' | |
| layer: 'ClusterGroundtruth' | |
| param_str : '1248, 352, 16, 1' | |
| } | |
| include: { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| type: 'Python' | |
| name: 'score' | |
| bottom: 'bbox-list-label' | |
| bottom: 'bbox-list' | |
| top: 'bbox-list-scored' | |
| python_param { | |
| module: 'caffe.layers.detectnet.mean_ap' | |
| layer: 'ScoreDetections' | |
| } | |
| include: { phase: TEST stage: "val" } | |
| } | |
| layer { | |
| type: 'Python' | |
| name: 'mAP' | |
| bottom: 'bbox-list-scored' | |
| top: 'mAP' | |
| top: 'precision' | |
| top: 'recall' | |
| python_param { | |
| module: 'caffe.layers.detectnet.mean_ap' | |
| layer: 'mAP' | |
| param_str : '1248, 352, 16' | |
| } | |
| include: { phase: TEST stage: "val" } | |
| } |
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